Vehicle exterior environment recognition apparatus

ABSTRACT

A vehicle exterior environment recognition apparatus includes a three-dimensional object region identifier and a specific part identifier. The three-dimensional object region identifier identifies a three-dimensional object region by monocular recognition based on a luminance image. The three-dimensional object region includes a three-dimensional object. The luminance image is generated by an image capturing unit that captures an image of vehicle exterior environment. The specific part identifier correlates the three-dimensional object region with a distance image, to identify a specific part of the three-dimensional object region on the basis of distance information. The distance image is generated from the luminance image. The distance information is calculated on the basis of the distance image.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority from Japanese Patent ApplicationNo. 2017-144321 filed on Jul. 26, 2017, the entire contents of which arehereby incorporated by reference.

BACKGROUND

The technology relates to a vehicle exterior environment recognitionapparatus that identifies a specific object present in a travelingdirection of an own vehicle.

A technique has been known that includes detecting a three-dimensionalobject, such as a vehicle located ahead of an own vehicle, andperforming a control to avoid collision with a preceding vehicle (i.e.,a collision avoidance control) or performing a control to keep a safeinter-vehicular distance from the preceding vehicle (i.e., a cruisecontrol). For example, reference is made to Japanese Patent No. 3349060.

As a technique to detect the three-dimensional object, JapaneseUnexamined Patent Application Publication (JP-A) No. 2008-134877discloses a technique that includes detecting a parallel-travelingvehicle that travels parallel with the own vehicle, with reference to animage pattern photographed sideward of the own vehicle, on the basis ofedge symmetry in a front-rear direction of the own vehicle.

SUMMARY

An aspect of the technology provides a vehicle exterior environmentrecognition apparatus that includes a three-dimensional object regionidentifier and a specific part identifier. The three-dimensional objectregion identifier is configured to identify a three-dimensional objectregion by monocular recognition based on a luminance image. Thethree-dimensional object region includes a three-dimensional object. Theluminance image is generated by an image capturing unit configured tocapture an image of vehicle exterior environment. The specific partidentifier is configured to correlate the three-dimensional objectregion with a distance image, to identify a specific part of thethree-dimensional object region on the basis of distance information.The distance image is generated from the luminance image. The distanceinformation is calculated on the basis of the distance image.

An aspect of the technology provides a vehicle exterior environmentrecognition apparatus that includes circuitry. The circuitry isconfigured to identify a three-dimensional object region by monocularrecognition based on a luminance image. The three-dimensional objectregion includes a three-dimensional object. The luminance image isgenerated by an image capturing unit configured to capture an image ofvehicle exterior environment. The circuitry is configured to correlatethe three-dimensional object region with a distance image, to identify aspecific part of the three-dimensional object region on the basis ofdistance information. The distance image is generated from the luminanceimage. The distance information is calculated on the basis of thedistance image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a relation of connection in avehicle exterior environment recognition system.

FIGS. 2A and 2B respectively describe a luminance image and a distanceimage.

FIG. 3 is a functional block diagram illustrating schematic functions ofa vehicle exterior environment recognition apparatus.

FIG. 4 is a flowchart illustrating an example of a flow of a vehicleexterior environment recognition process.

FIG. 5A, FIG. 5B and FIG. 5C describe examples of a three-dimensionalobject region identification process.

FIG. 6A, FIG. 6B and FIG. 6C describe examples of a specific partidentification process.

DETAILED DESCRIPTION

In the following, some preferred but non-limiting implementations of thetechnology are described in detail with reference to the accompanyingdrawings. Note that sizes, materials, specific values, and any otherfactors illustrated in respective implementations are illustrative foreasier understanding of the technology, and are not intended to limitthe scope of the technology unless otherwise specifically stated.Further, elements in the following example implementations which are notrecited in a most-generic independent claim of the disclosure areoptional and may be provided on an as-needed basis. The drawings areschematic and are not intended to be drawn to scale. Throughout thepresent specification and the drawings, elements having substantiallythe same function and configuration are denoted with the same referencenumerals to avoid any redundant description. Further, elements that arenot directly related to the technology are unillustrated in thedrawings.

Non-limiting examples of a specific object present in a travelingdirection of an own vehicle may include a preceding vehicle that travelsin a same direction, and objects such as a pedestrian, i.e., a human,and a bicycle that cross a traveling path in a lateral direction of theown vehicle. Regarding the objects such as the pedestrian and thebicycle that cross the traveling path, it is desirable to determinetheir pedestrian-likeliness or bicycle-likeliness, on the basis of theiroutlines. In many cases, however, a pedestrian is smaller in absolutevolume and more unstable in behavior, as compared to a vehicle or abicycle. If a collision avoidance control is postponed untilconfirmation of presence of the pedestrian on the basis of, for example,their entire outline, a distance from the own vehicle to the pedestrianmay become short during the postponement. This may necessitate an abruptaction as the collision avoidance control.

In particular, there are cases where a pedestrian jumps into thetraveling path from behind a three-dimensional object such as a vehicle.In such cases, while a relative distance from the pedestrian to the ownvehicle takes a continuous value with a relative distance from thethree-dimensional object to the own vehicle, it is difficult todistinguish the pedestrian from the three-dimensional object solely onthe basis of distance information. This may result in difficulty inearly detection of the pedestrian. As used herein, the distanceinformation refers to information regarding the relative distance asmentioned above.

It is desirable to provide a vehicle exterior environment recognitionapparatus that makes it possible to detect a specific object such as apedestrian early.

Vehicle Exterior Environment Recognition System 100

FIG. 1 is a block diagram illustrating a relation of connection in avehicle exterior environment recognition system 100. The vehicleexterior environment recognition system 100 may include image-capturingunits 110, a vehicle exterior environment recognition apparatus 120, anda vehicle controller (e.g., an engine control unit (ECU)) 130. Theimplementation may include two image-capturing units 110 withoutlimitation.

The two image-capturing units 110 may each include an imaging devicesuch as, but not limited to, a charge-coupled device (CCD) and acomplementary metal-oxide semiconductor (CMOS). The image-capturingunits 110 may each be able to capture an image of vehicle exteriorenvironment ahead of the own vehicle 1, and to generate a luminanceimage that includes at least information on luminance. The luminanceimage may be a color image or a monochrome image. The twoimage-capturing units 110 may be so disposed that their respectiveoptical axes become substantially parallel to each other along atraveling direction of the own vehicle 1. The two image-capturing units110 may be so disposed as to be separated away from each other in asubstantially horizontal direction. The image-capturing units 110 maycontinuously generate the luminance image for each frame of, for examplebut not limited to, 1/60 second (at a frame rate of 60 fps). Theluminance image may be an image that captures a three-dimensional objectpresent in a detected region ahead of the own vehicle 1. Non-limitingexamples of the three-dimensional objects to be recognized by theimage-capturing units 110 may include a three-dimensional object that ispresent independently, and an object as a part of theindependently-present object. Non-limiting examples of theindependently-present object may include a bicycle, a pedestrian (or ahuman), a vehicle, a traffic light, a road (or a traveling path), a roadsign, a guardrail, and a building. Non-limiting examples of the objectas a part of the independently-present object may include a part of abody of a pedestrian, e.g., a head or shoulders.

The vehicle exterior environment recognition apparatus 120 may obtainthe luminance images from the respective image-capturing units 110, andderive parallax information with use of so-called pattern matching. Thepattern matching may involve extracting any block (e.g., an array of 4pixels horizontally by 4 pixels vertically) from one of the luminanceimages, and searching for a corresponding block in another of theluminance images. The parallax information may include a parallax, andan on-screen position of any block. The on-screen position indicates aposition of any block on a screen. In this implementation, the term“horizontally” refers to an on-screen lateral direction of the capturedimage, while the term “vertically” refers to an on-screen verticaldirection of the captured image. A possible example of the patternmatching may be to compare a pair of images in terms of luminance (Y)block by block. Non-limiting examples may include techniques such as SAD(Sum of Absolute Difference), SSD (Sum of Squared intensity Difference),and ZNCC (Zero-mean Normalized Cross Correlation). The SAD includesobtaining differences in the luminance. The SSD includes using thedifferences squared. The ZNCC includes obtaining similarity of variancevalues obtained by subtracting an average value from luminance values ofpixels. The vehicle exterior environment recognition apparatus 120 mayperform such a block-by-block parallax derivation process, for allblocks displayed in the detected region of, for example, 600 pixels by200 pixels. In this implementation, one block is assumed to be the arrayof 4 pixels by 4 pixels, but the number of the pixels inside one blockmay be set at any value.

It is to be noted that the vehicle exterior environment recognitionapparatus 120 is able to derive the parallax for each of the blocks, butthe vehicle exterior environment recognition apparatus 120 is not ableto recognize what kind of object each of the blocks belongs to. Theblock serves as a unit of detection resolution. It follows, therefore,that the parallax information is derived not by the object butindependently by the detection resolution in the detected region, e.g.,by the block. In this implementation, an image with which the parallaxinformation thus derived is correlated is referred to as a distanceimage, in distinction from the luminance image as mentioned above.

FIGS. 2A and 2B respectively describe the luminance image 126 and thedistance image 128. FIG. 2A describes a non-limiting example in whichthe luminance image 126 as illustrated in FIG. 2A is generated for thedetected region 124 by means of the two image-capturing units 110. Notethat FIG. 2A schematically illustrates only one of the two luminanceimages 126 generated by the respective image-capturing units 110 foreasier understanding. The vehicle exterior environment recognitionapparatus 120 may obtain the parallax for each of the blocks from theluminance images 126 to form the distance image 128 as illustrated inFIG. 2B. Each of the blocks in the distance image 128 may be associatedwith the parallax of the relevant block. For description purpose, eachof the blocks for which the parallax is derived is denoted by a blackdot.

Moreover, the vehicle exterior environment recognition apparatus 120 mayperform grouping of blocks, as an object. The grouping may be made withthe use of luminance values, i.e., color values, based on the luminanceimage 126, and with the use of three-dimensional positional informationin real space. The three-dimensional positional information may becalculated on the basis of the distance image 128, and include arelative distance to the own vehicle 1. The blocks to be grouped may beof equal color values, and of close relative distances included in thethree-dimensional positional information. The vehicle exteriorenvironment recognition apparatus 120 may identify which specific objectthe object in the detected region ahead of the own vehicle 1 correspondsto. Non-limiting example of the specific object may include a precedingvehicle and a pedestrian. Moreover, upon identifying thethree-dimensional object in this way, the vehicle exterior environmentrecognition apparatus 120 may further control the own vehicle 1, toavoid collision with the three-dimensional object (i.e., the collisionavoidance control) or to keep a safe inter-vehicular distance from thepreceding vehicle (i.e., a cruise control). Note that the relativedistance as mentioned above may be obtained by converting the parallaxinformation for each of the blocks in the distance image 128 to thethree-dimensional positional information with the use of a so-calledstereo method. In this implementation, the stereo method refers to amethod of deriving, from the parallax of the object, the relativedistance of the relevant object with respect to the image-capturingunits 110, with the use of triangulation.

Returning to FIG. 1, the vehicle controller 130 may control the ownvehicle 1 by accepting an operation input of the driver through asteering wheel 132, an accelerator pedal 134, and a brake pedal 136 andtransmitting the operation input to a steering mechanism 142, a drivemechanism 144, and a brake mechanism 146. The vehicle controller 130 maycontrol the steering mechanism 142, the drive mechanism 144, and thebrake mechanism 146, in accordance with instructions from the vehicleexterior environment recognition apparatus 120.

In the following, described in detail is a configuration of the vehicleexterior environment recognition apparatus 120. A description is givenhere in detail of an identification process of the three-dimensionalobject (e.g., a pedestrian) in the detected region ahead of the ownvehicle 1. Note that a configuration less related to features of theimplementation will not be described in detail.

Vehicle Exterior Environment Recognition Apparatus 120

FIG. 3 is a functional block diagram illustrating schematic functions ofthe vehicle exterior environment recognition apparatus 120. Referring toFIG. 3, the vehicle exterior environment recognition apparatus 120 mayinclude an interface (I/F) 150, a data storage 152, and a centralcontroller 154.

The interface 150 may be an interface that exchanges informationbi-directionally between devices including, without limitation, theimage-capturing units 110 and the vehicle controller 130. The datastorage 152 may include a random access memory (RAM), a flash memory, ahard disk drive (HDD), or any other suitable storage device. The datastorage 152 may store various pieces of information necessary forprocesses to be carried out by the functional blocks to be describedhereinafter.

The central controller 154 may include a semiconductor integratedcircuit, and control devices including, without limitation, theinterface 150 and the data storage 152 through a system bus 156. Thesemiconductor integrated circuit may have devices such as, but notlimited to, a central processing unit (CPU), a read only memory (ROM) inwhich programs, etc., are stored, and a random access memory (RAM)serving as a work area. In this implementation, the central controller154 may function as a three-dimensional object region identifier 160, aspecific part identifier 162, a speed-of-movement deriving unit 164, anda collision avoidance control unit 166. In the following, a detaileddescription is given, on the basis of operation of each functional blockof the central controller 154 as well, of a vehicle exterior environmentrecognition process that involves, as a feature of the implementation,recognizing a pedestrian, i.e., a human.

Vehicle Exterior Environment Recognition Process

FIG. 4 is a flowchart illustrating an example of a flow of the vehicleexterior environment recognition process. The vehicle exteriorenvironment recognition process may involve execution of the followingprocesses: a three-dimensional object region identification process(S200); a specific part identification process (S202); aspeed-of-movement derivation process (S204); and a collision avoidancecontrol process (S206). In the three-dimensional object regionidentification process (S200), the three-dimensional object regionidentifier 160 identifies a three-dimensional object region by monocularrecognition based on the luminance image 126. The three-dimensionalobject region includes the three-dimensional object, e.g., a pedestrian.In the specific part identification process (S202), the specific partidentifier 162 correlates the three-dimensional object region with thedistance image 128, to identify a specific part of the three-dimensionalobject region on the basis of the distance information. In thespeed-of-movement derivation process (S204), the speed-of-movementderiving unit 164 may derive a speed of movement of the specific partidentified. Lastly, in the collision avoidance control process (S206),the collision avoidance control unit 166 may execute the collisionavoidance control. It is to be noted that the vehicle exteriorenvironment recognition process may be repetitively executed for eachframe of acquisition of the luminance image 126 and the distance image128.

Three-Dimensional Object Region Identification Process S200

FIGS. 5A-5C describe examples of the three-dimensional object regionidentification process S200. Described first is an attempt atidentifying a pedestrian on the basis of the distance image 128illustrated in FIG. 5A. This attempt assumes a case where athree-dimensional object 212 jumps from behind a three-dimensionalobject 210 located in the distance image 128. The three-dimensionalobject 212 corresponds to the pedestrian. The three-dimensional object210 corresponds to an automobile. The automobile and the pedestrian arein separate and distinct relation from each other. However, while adistance from the automobile to the pedestrian is small, as illustratedin FIG. 5B, the relative distance from the three-dimensional object 210corresponding to the automobile with respect to the own vehicle 1 takesa continuous value with the relative distance from the three-dimensionalobject 212 corresponding to the pedestrian with respect to the ownvehicle 1. Accordingly, detecting a three-dimensional object on thebasis of the distance image 128 causes a large three-dimensional objectregion 214 to be formed as illustrated in FIG. 5A. The largethree-dimensional object region 214 includes both the three-dimensionalobject 210 corresponding to the automobile and the three-dimensionalobject 212 corresponding to the pedestrian. This makes it difficult todistinguish the pedestrian from the automobile.

Described now is another attempt at identifying the pedestrian on thebasis of the luminance image 126, instead of the distance image 128. Inone specific but non-limiting example, as illustrated in FIG. 5C, thepedestrian is identified, employing a recognition technique thatincludes recognizing a specific object with the use of machine learningon the basis of a shape or a pattern of any image in a monocular image,i.e., solely in one of the two luminance images 126. This recognitiontechnique is hereinafter simply referred to as the “monocularrecognition”. In this case, as illustrated in FIG. 5C, athree-dimensional object region 216 is formed that appropriatelyincludes solely the pedestrian.

The monocular recognition as mentioned above identifies thethree-dimensional object as the pedestrian with high probability, butprecision of identification of a position of the three-dimensionalobject is not so high. Moreover, a shape of the three-dimensional objectregion 216 easily changes in accordance with behavior of the pedestrian.Therefore, the vehicle exterior environment recognition system 100 isable to grasp presence of the pedestrian on the traveling path, but mayhave difficulty in accurately identifying a speed of movement of thepedestrian. This may cause possibility of instability of the collisionavoidance control with the pedestrian.

What is desired in this implementation is, therefore, to effectivelyunite identification of a three-dimensional object as a pedestrian bythe monocular recognition, with identification of a position or a speedof movement of the pedestrian with the use of the distance image 128, todetect a specific object such as a pedestrian early and stably.

Accordingly, as illustrated in FIG. 5C, the three-dimensional objectregion identifier 160, first, identifies the three-dimensional objectregion 216 by the monocular recognition based on the luminance image126. The three-dimensional object region 216 includes the pedestrian.However, the three-dimensional object region identifier 160 may refrainfrom deriving the speed of movement of the pedestrian from a result ofthe monocular recognition.

Specific Part Identification Process S202

FIGS. 6A-6C describe examples of the specific part identificationprocess S202. The specific part identifier 162 correlates thethree-dimensional object region 216 just as identified on the luminanceimage 126 by the monocular recognition, with the distance image 128, toa corresponding position of the distance image 128. Thus, as illustratedin FIG. 6A, the three-dimensional object region 216 is formed on thedistance image 128. The three-dimensional object region 216 on thedistance image 128 is identical to that on the luminance image 126. Inother words, a shape and area of the three-dimensional object region 216on the distance image 128 are identical to those on the luminance image126.

Thereafter, as illustrated in FIG. 6B, the specific part identifier 162may equally divide the three-dimensional object region 216 on thedistance image 128 into a predetermined number of divisions. In thisexample, the specific part identifier 162 may equally divide thethree-dimensional object region 216 on the distance image 128 into, forexample, eight vertically-arranged divisions each of which is shaped ofa laterally-disposed strip. The specific part identifier 162 may extractthe three-dimensional object 212 included in a division in apredetermined ordinal number from top of the screen. In this example,the specific part identifier 162 may extract the three-dimensionalobject 212 included in a division in the second place from the top, asillustrated in FIG. 6C. In this implementation, the place of thedivision to be extracted is decided on an assumption that the shouldersof the pedestrian are located in the division in the second place fromthe top, among the eight vertically-arranged divisions of thepedestrian.

Thereafter, the specific part identifier 162 may identify a left end anda right end of a segment having the distance information, i.e., pixelsor blocks having the distance information, out of the extracteddivision. In this implementation, the segment having the distanceinformation refers to a segment the relative distance of which fallswithin a predetermined range with reference to an average relativedistance of the three-dimensional object 212 with respect to the ownvehicle 1. The predetermined range may be, for example, ±1 meter. It isto be noted that in a case where the extracted division includes nosegment having the distance information, a determination may be madethat the relevant three-dimensional object 212 is not a pedestrian, andthe vehicle exterior environment recognition process may be terminated.

Thereafter, as illustrated in FIG. 6C, the specific part identifier 162may identify, as a specific part 218, a point that is positionedhorizontally in the middle of the left end and the right end thusidentified, and is positioned vertically in the middle of the extracteddivision.

Speed-of-Movement Derivation Process S204

The speed-of-movement deriving unit 164 may derive a direction ofmovement and the speed of movement of the specific part 218. Thederivation may be made on the basis of a difference between a positionof the lately-identified specific part 218 on the distance image 128 anda position of the preceding-identified specific part 218 on the distanceimage 128, and on the basis of the relative distances thereof. Thespeed-of-movement deriving unit 164 may store the lately-identifiedspecific part 218 to update a next-time preceding value.

Collision Avoidance Control Process S206

On the ground that the three-dimensional object region 216 identified bythe three-dimensional object region identifier 160 includes apedestrian, and that the pedestrian is moving in the direction ofmovement and at the speed of movement derived by the speed-of-movementderiving unit 164, the collision avoidance control unit 166 may executethe collision avoidance control, in order to avoid the collision withthe pedestrian.

As described, in this implementation, first, the three-dimensionalobject is identified as a pedestrian by the monocular recognition. Thismakes it possible to detect the pedestrian earlier, as compared to acase solely with the use of the distance information. Hence, it ispossible to detect a specific object such as a pedestrian early.

Moreover, the position and the speed of movement of the pedestrian maybe derived with the use of the distance image 128, without dependingsolely on the monocular recognition. This makes it possible to enhanceprecision of the identification of the position and the speed ofmovement. Hence, it is possible to detect the specific object such as apedestrian early and stably.

Furthermore, in identifying the position of the pedestrian, the verticalposition of the pedestrian may be set at a level corresponding to theshoulders of a human, i.e., a level between the neck and the chest.Because the shoulders are less likely to shift from a central axis of ahuman body, as compared to the head or the legs, it is possible toenhance the precision of the identification. It is to be noted that alumbar part may serve as an alternative because the lumbar part is alsounlikely to shift from the central axis of the human body. However, alocus of the lumbar part is sometimes unstable under an influence ofarms and hands that move back and forth because of walking. Accordingly,it would be desirable to use the shoulders.

In addition, in identifying the position of the pedestrian, thehorizontal position of the pedestrian may be set at a positioncorresponding to a midpoint of the shoulders of the human. Hence, it ispossible to acquire a stable locus of movement, even in a case where theshoulders move back and forth because of walking.

The implementation also provides a program that causes a computer tofunction as the vehicle exterior environment recognition apparatus 120,and a non-transitory recording medium that stores the program. Thenon-transitory recording medium is computer readable. Non-limitingexamples of the non-transitory recording medium may include a flexibledisk, a magneto-optical disk, ROM, CD, DVD (Registered Trademark), andBD (Registered Trademark). As used herein, the term “program” may referto a data processor written in any language and any description method.

Although some preferred implementations of the technology have beendescribed in the foregoing by way of example with reference to theaccompanying drawings, the technology is by no means limited to theimplementations described above. It should be appreciated thatmodifications and alterations may be made by persons skilled in the artwithout departing from the scope as defined by the appended claims. Thetechnology is intended to include such modifications and alterations inso far as they fall within the scope of the appended claims or theequivalents thereof.

For instance, in one implementation described above, the description ismade by giving an example where the three-dimensional object may be apedestrian, or a human. The specific part of the three-dimensionalobject may be the horizontal midpoint between the vertical positionsthat correspond to the shoulders of the pedestrian. However, thetechnology is not limited to such an implementation. The technology maybe targeted at various three-dimensional objects that are eligible to betargets of the monocular recognition, e.g., a bicycle, a motorcycle, andan automobile.

In one implementation described above, the description is made on anexample where the specific part is vertically positioned at the levelcorresponding to the shoulders of the pedestrian. Specifically, thethree-dimensional object region 216 is equally divided into the eightvertically-arranged divisions, and the division in the second place fromthe top is extracted. However, the number of the divisions and the placeof the division to be extracted are not limited to as described above,and various values may be adopted. For example, the three-dimensionalobject region 216 may be equally divided into four divisions, and anuppermost division may be extracted. In another alternative, thethree-dimensional object region 216 may be equally divided into fivedivisions, and a division in the second place from the top may beextracted.

A part or all of the processes in the vehicle exterior environmentrecognition process as disclosed herein does not necessarily have to beprocessed on a time-series basis in the order described in the exampleflowchart. A part or all of the processes in the vehicle exteriorenvironment recognition process may involve parallel processing orprocessing based on subroutine.

The central controller 154 illustrated in FIG. 3 is implementable bycircuitry including at least one semiconductor integrated circuit suchas at least one processor (e.g., a central processing unit (CPU)), atleast one application specific integrated circuit (ASIC), and/or atleast one field programmable gate array (FPGA). At least one processoris configurable, by reading instructions from at least one machinereadable non-transitory tangible medium, to perform all or a part offunctions of the central controller 154. Such a medium may take manyforms, including, but not limited to, any type of magnetic medium suchas a hard disk, any type of optical medium such as a compact disc (CD)and a digital video disc (DVD), any type of semiconductor memory (i.e.,semiconductor circuit) such as a volatile memory and a non-volatilememory. The volatile memory may include a dynamic random access memory(DRAM) and a static random access memory (SRAM), and the non-volatilememory may include a ROM and a non-volatile RAM (NVRAM). The ASIC is anintegrated circuit (IC) customized to perform, and the FPGA is anintegrated circuit designed to be configured after manufacturing inorder to perform, all or a part of the functions of the centralcontroller 154 illustrated in FIG. 3.

Although some implementations of the technology have been described inthe foregoing by way of example with reference to the accompanyingdrawings, the technology is by no means limited to the implementationsdescribed above. The use of the terms first, second, etc. does notdenote any order or importance, but rather the terms first, second, etc.are used to distinguish one element from another. It should beappreciated that modifications and alterations may be made by personsskilled in the art without departing from the scope as defined by theappended claims. The technology is intended to include suchmodifications and alterations in so far as they fall within the scope ofthe appended claims or the equivalents thereof.

1. A vehicle exterior environment recognition apparatus, comprising: athree-dimensional object region identifier configured to identify athree-dimensional object region by monocular recognition based on aluminance image, the three-dimensional object region including athree-dimensional object, and the luminance image being generated by animage capturing unit configured to capture an image of vehicle exteriorenvironment; and a specific part identifier configured to correlate thethree-dimensional object region with a distance image, to identify aspecific part of the three-dimensional object region on a basis ofdistance information, the distance image being generated from theluminance image, and the distance information being calculated on abasis of the distance image.
 2. The vehicle exterior environmentrecognition apparatus according to claim 1, further comprising aspeed-of-movement deriving unit configured to derive, on the basis ofthe distance image, a speed of movement of the specific part identified.3. The vehicle exterior environment recognition apparatus according toclaim 1, wherein the three-dimensional object is a human, and thespecific part is a horizontal midpoint between vertical positions thatcorrespond to shoulders of the human.
 4. The vehicle exteriorenvironment recognition apparatus according to claim 2, wherein thethree-dimensional object is a human, and the specific part is ahorizontal midpoint between vertical positions that correspond toshoulders of the human.
 5. A vehicle exterior environment recognitionapparatus, comprising circuitry configured to identify athree-dimensional object region by monocular recognition based on aluminance image, the three-dimensional object region including athree-dimensional object, and the luminance image being generated by animage capturing unit configured to capture an image of vehicle exteriorenvironment, and correlate the three-dimensional object region with adistance image, to identify a specific part of the three-dimensionalobject region on a basis of distance information, the distance imagebeing generated from the luminance image, and the distance informationbeing calculated on a basis of the distance image.